669 research outputs found
High-Precision Spectroscopy with Counter-Propagating Femtosecond Pulses
An experimental realization of high-precision direct frequency comb
spectroscopy using counter-propagating femtosecond pulses on two-photon atomic
transitions is presented. Doppler broadened background signal, hampering
precision spectroscopy with ultrashort pulses, is effectively eliminated with a
simple pulse shaping method. As a result, all four 5S-7S two-photon transitions
in a rubidium vapor are determined with both statistical and systematic
uncertainties below 10, which is an order of magnitude better than
previous experiments on these transitions.Comment: 5 pages, 4 figures. Accepted to PR
Selecting surface features for accurate multi-camera surface reconstruction
This paper proposes a novel feature detector for selecting local textures that are suitable for accurate multi-camera surface reconstruction, and in particular planar patch fitting techniques. This approach is in contrast to conventional feature detectors, which focus on repeatability under scale and affine transformations rather than suitability for multi-camera reconstruction techniques. The proposed detector selects local textures that are sensitive to affine transformations, which is a fundamental requirement for accurate patch fitting. The proposed detector is evaluated against the SIFT detector on a synthetic dataset and the fitted patches are compared against ground truth. The experiments show that patches originating from the proposed detector are fitted more accurately to the visible surfaces than those originating from SIFT keypoints. In addition, the detector is evaluated on a performance capture studio dataset to show the real-world application of the proposed detector
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation
Recent studies have revealed a number of pathologies of neural machine
translation (NMT) systems. Hypotheses explaining these mostly suggest that
there is something fundamentally wrong with NMT as a model or its training
algorithm, maximum likelihood estimation (MLE). Most of this evidence was
gathered using maximum a posteriori (MAP) decoding, a decision rule aimed at
identifying the highest-scoring translation, i.e. the mode, under the model
distribution. We argue that the evidence corroborates the inadequacy of MAP
decoding more than casts doubt on the model and its training algorithm. In this
work, we criticise NMT models probabilistically showing that stochastic samples
following the model's own generative story do reproduce various statistics of
the training data well, but that it is beam search that strays from such
statistics. We show that some of the known pathologies of NMT are due to MAP
decoding and not to NMT's statistical assumptions nor MLE. In particular, we
show that the most likely translations under the model accumulate so little
probability mass that the mode can be considered essentially arbitrary. We
therefore advocate for the use of decision rules that take into account
statistics gathered from the model distribution holistically. As a proof of
concept we show that a straightforward implementation of minimum Bayes risk
decoding gives good results outperforming beam search using as little as 30
samples, confirming that MLE-trained NMT models do capture important aspects of
translation well in expectation
Auto-Encoding Variational Neural Machine Translation
We present a deep generative model of bilingual sentence pairs for machine
translation. The model generates source and target sentences jointly from a
shared latent representation and is parameterised by neural networks. We
perform efficient training using amortised variational inference and
reparameterised gradients. Additionally, we discuss the statistical
implications of joint modelling and propose an efficient approximation to
maximum a posteriori decoding for fast test-time predictions. We demonstrate
the effectiveness of our model in three machine translation scenarios:
in-domain training, mixed-domain training, and learning from a mix of
gold-standard and synthetic data. Our experiments show consistently that our
joint formulation outperforms conditional modelling (i.e. standard neural
machine translation) in all such scenarios
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